Author

Tzu‐Hsi Song

Worcester Polytechnic InstituteORCID 0000-0002-3670-8970Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 1 Approach:deep learning· 1 Organism:human· 1 Assay:imaging· 1

Frequent Co-Authors

Hakho LeeiD· 1 shared Hyungsoon ImiD· 1 shared Jouha MiniD· 1 shared Kwonmoo LeeiD· 1 shared Mengzhi CaoiD· 1 shared

Activity by Year

2021-2021 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 10 mutual co-author links

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1 shared paperBioinformatics

Hakho LeeORCID

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1 paper

Recent·Most cited

Interpretable Deep Learning for Breast Cancer Cell Phenotyping Using Diffraction Images from Lens-Free Digital In-Line Holography

Tzu‐Hsi Song, Mengzhi Cao, Jouha Min, Hyungsoon Im, Hakho Lee, Kwonmoo Lee

HoloNet is a deep learning model that analyzes holographic images of breast cancer cells to classify cell types and quantify molecular markers, identifying new subtypes.

bioRxiv · May 29, 2021 · PreprintBioinformatics